TURF Breathing new life into an old technique

1. TURFBreathing new life into an old technique Ray Poynter Director, Virtual Surveys

2. A typical research problem • Gelati & Sons make ice cream • In a typical store they sell 8 flavours and they have lots of data about how well they sell • They have a new contract to supply a national supermarket • But they are only allowed to offer 4 flavours • Which flavours? • The simple answer? • The best selling 4 • The research answer • TURF – Total Unduplicated Reach and Frequency

3. TURF – a bit of background • Dates back to the late 80s • Many research companies offer it in their toolkit • Only a handful of papers over the last 20 years • Rarely used these days • BUT • With a dusting of Internet-based data collection • And exposure to Excel-based modelling • A powerful tool for portfolio management

4. R2 1 1 0 R3 1 1 0 R4 1 0 0 Customers 4 Almond + Banana = 4 happy customers (total unduplicated reach = 4) Almond + Coffee = 5 happy customers (total unduplicated reach = 5) Why TURF? • Consider the matrix below, with 3 flavours • The data shows whether a flavour is bought by each respondent Almond Banana Coffee R1 1 1 1 R5 0 0 1 3 2

5. Gelati & Sons There are 70 different ways to choose 4 flavours from these 8, which 4 maximise the reach?

6. Solver • Excel Add-in • Check you have the Solver Add-In enabled • Choose a cell to maximise • The Reach value in our case • Create constraints • Each flavour is either in or out (integer values in the range 0 to 1) • The number of flavours must equal the number requested • Solver will then search for the best solution

7. Number of flavours wanted Reach achieved, the value maximised Solver adjusts these values constraining them to be 0 or 1 Constrains the number of 1s, to number wanted Solver example 1

8. Different scenarios Sub-samples can easily be set up: Either as sample selections Or, as separate Excel pages, one per key sub-sample

9. Gelati & Sons • Almond Ice Cream • €2.95 • How likely are you to buy this ice cream some of the time? • Definitely buy • Probably buy • May or may not buy • Probably won’t buy • Definitely won’t buy • Gelati & Sons • Almond Ice Cream • €2.95 • How often will you probably buy this ice cream? • 5-7 times a week • 2-4 times a week • Once a week • 2-3 times a month • Once a month • Every 2-3 months • Less often Simple to Collect • Each respondent sees all the scenarios, randomised If definitely or probably buy

10. Frequency, that’s why it’s not TUR • Only people who are going to buy the product have a frequency greater than 0 • Definitely buys have a frequency • Probably buys have a frequency only if you are counting probably buy as people who are buying • Frequencies need converting to a common base • In our example we might use the values as purchases per year • Frequencies may need re-scaling • Ideally using calibration data or norms • Rough rule of thumb • Square root of definite buy frequencies • Cube root of probably buy frequencies

11. Choice and Frequency • The questions were monadic • So, what do we do if we have a respondent who says • If Almond is offered I will buy 4 per year • If Banana is offered I will buy 12 per year • If we offer him Almond and Banana? • If the products are comparable? • As in this example • Usually safe to assume he/she will buy 12 products • Some unknown mixture of Almond and Banana • If necessary, keep the ratios, e.g. Almond 3, Banana 9 • If the products are not substitutable? • e.g. some last longer, or are twice as big • Then more complex assumptions have to be used

12. p.a. Almond Banana Coffee Almond & Banana Almond & Coffee Banana & Coffee R1 5 15 8 15 8 15 R2 4 12 0 12 4 12 R3 3 8 0 8 3 8 R4 3 0 0 3 3 0 R5 0 0 7 0 7 7 38 25 42 Simple example, re-visited • Almond has more people who would buy, but they would buy less • Almond & Coffee meets everyone’s needs, but with the lowest frequency • Banana & Coffee has the highest predicted frequency

13. Value to maximise Solving for Frequency The system can be set up to report reach as well as frequency, along with sub-groups etc.

14. Frequency solutions

15. Improving the interface • By using customised VBA and Solver a more complete solution includes: • Selection of sub-groups • Dynamically switching between Definite Buys only and Definite plus Probably Buy • Stepwise solution of 1 to N products, reporting reach, frequency, cumulative reach and cumulative frequency • Dynamically switching between Reach and Frequency • Ability to temporarily exclude products • Ability to force specific products • Ability to weight key sub-groups, e.g. to make it much more likely that longstanding customers will have a product they definitely like

16. The client experience • Whilst traditional TURF approaches provide useful insight, it has often been static and dull • What-if modelling allows the client to really understand the dynamics • Extensions include: • Adding Value weights to the products • Forcing specific items to be selected • Asking for the next best solution • Identifying the disenfranchised • Modifying the rules so a solution that finds 2 products for each respondent • Multiple ranges, e.g. in chilled food the best ranges of Indian, Chinese, Mexican, and Italian

17. Definites versus Probables • Should the analysis be based on Probably Buy or on both Probably and Definitely Buy? • Cases vary but: • Which option is closest to sales data? • Try it both ways, see what the difference is • If you are getting enough definites use these • If you are using frequency then either use only definites or down weight the probably frequencies

18. Key TURF Questions • Why isn’t TURF used more? • Perhaps because it is a specific tool for a specific problem and is not readily converted into a general tool • How might technology impact TURF? • HB might remove the need for each respondent to evaluate all the scenarios • When is TURF applicable? • Flavours • Products in a vending machine • Travel and ticket options • Pack and size variants (with care) • Courses (including conferences) • Menus and bundles

19. Thank you Questions?